1. Field
This disclosure relates generally to reducing detection complexity in a communication system and, more specifically, to techniques for reducing joint detection complexity in a channel-coded multiple-input multiple-output communication system.
2. Related Art
Today, multiple-input multiple-output (MIMO) systems, which employ multiple antennas at a transmitter and multiple antennas at a single receiver or one or more antennas at multiple receivers (depending on the implementation), are becoming increasingly common. Single-user MIMO systems implement multiple antennas at a transmitter and multiple antennas at a receiver. In contrast, multi-user MIMO systems employ multiple antennas at a transmitter and consider multiple receivers (each of which may have one or more antennas) as spatial resources, with each of the multiple receivers corresponding to at least one output. In general, MIMO wireless communication systems exhibit increased data throughput (due to higher spectral efficiency) and increased link range (due to reduced fading) without requiring additional bandwidth or transmit power, respectively (as contrasted with multiple-input single-output (MISO), single-input multiple-output (SIMO), and single-input single-output (SISO) wireless communication systems). MIMO wireless communication systems generally employ precoding, spatial multiplexing (SM), diversity coding, a combination of SM and precoding, or a combination of SM and diversity coding.
In SM (which can be employed with or without channel state information (CSI) at a transmitter), an original signal is split into multiple lower-rate streams and each stream is transmitted from a different transmit antenna in the same frequency band. When the transmitted streams arrive at a receiver antenna array with sufficiently different spatial signatures, a MIMO receiver can separate the transmitted streams into parallel channels that exhibit increased signal-to-noise ratio (SNR), as compared to the original signal when transmitted as a single higher-rate stream.
Precoding employs beamforming to support multi-layer communications. Precoding normally utilizes knowledge of CSI at a transmitter in an attempt to maximize received signal levels at all antennas of a receiver. Precoding can be generally defined as a transformation applied to transmitted data before transmission, typically to align the transmission to a channel in some form to maximize a performance metric, e.g., SNR. Precoding, in general, can be a linear or non-linear transformation. In linear precoding, the transformation can be equivalently applied in the form of a matrix to the transmitted vector symbol on the multiple antennas. Typically, some form of channel knowledge is used at the transmitter to choose an appropriate precoder. In some cases, a receiver feeds back information about a channel or a precoder to a transmitter.
Various wireless networks, such as third-generation partnership project long-term evolution (3GPP-LTE) and IEEE 802.16 (also known as worldwide interoperability for microwave access (WiMAX)) compliant architectures, employ a scheduler (included within or coupled to a serving base station (BS)) that utilizes information derived from channel characterization to determine channel allocation for served user equipment (subscriber stations (SSs)). In a 3GPP-LTE compliant system, channel allocation, e.g., uplink and downlink assignments, is provided to SSs over a downlink shared control channel (physical downlink control channel (PDCCH)), which typically includes one or more control channel symbols.
To increase capacity and performance of wireless communication systems, receivers of a communication system may employ joint (multiple user) detection. Joint detection is similar to solving a least squares (LS) problem, which may represent a significant computational effort due to the amount of data that may be involved. In general, joint detection at a serving BS combines knowledge about all subscriber stations (SSs) that are active in one burst in a relatively large system of equations. This knowledge has included channel impulse responses (that have been estimated from training sequences), spreading codes, and received antenna samples. Typically, designers have attempted to develop algorithms that lower computational complexity associated with joint detection (at a serving BS and at SSs) without significantly degrading joint detection performance. Traditionally, joint detection has been performed using time-domain approaches (in contrast to frequency-domain approaches), due to the lower complexity traditionally associated with time-domain approaches.